In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
%matplotlib inline
In [2]:
data = pd.read_csv("Sleep_health_and_lifestyle_dataset.csv")
In [3]:
data
Out[3]:
Person ID Gender Age Occupation Sleep Duration Quality of Sleep Physical Activity Level Stress Level BMI Category Blood Pressure Heart Rate Daily Steps Sleep Disorder
0 1 Male 27 Software Engineer 6.1 6 42 6 Overweight 126/83 77 4200 None
1 2 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 None
2 3 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 None
3 4 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
4 5 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
... ... ... ... ... ... ... ... ... ... ... ... ... ...
369 370 Female 59 Nurse 8.1 9 75 3 Overweight 140/95 68 7000 Sleep Apnea
370 371 Female 59 Nurse 8.0 9 75 3 Overweight 140/95 68 7000 Sleep Apnea
371 372 Female 59 Nurse 8.1 9 75 3 Overweight 140/95 68 7000 Sleep Apnea
372 373 Female 59 Nurse 8.1 9 75 3 Overweight 140/95 68 7000 Sleep Apnea
373 374 Female 59 Nurse 8.1 9 75 3 Overweight 140/95 68 7000 Sleep Apnea

374 rows × 13 columns

In [4]:
data.head()
Out[4]:
Person ID Gender Age Occupation Sleep Duration Quality of Sleep Physical Activity Level Stress Level BMI Category Blood Pressure Heart Rate Daily Steps Sleep Disorder
0 1 Male 27 Software Engineer 6.1 6 42 6 Overweight 126/83 77 4200 None
1 2 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 None
2 3 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 None
3 4 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
4 5 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
In [5]:
data.shape
Out[5]:
(374, 13)
In [6]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 374 entries, 0 to 373
Data columns (total 13 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   Person ID                374 non-null    int64  
 1   Gender                   374 non-null    object 
 2   Age                      374 non-null    int64  
 3   Occupation               374 non-null    object 
 4   Sleep Duration           374 non-null    float64
 5   Quality of Sleep         374 non-null    int64  
 6   Physical Activity Level  374 non-null    int64  
 7   Stress Level             374 non-null    int64  
 8   BMI Category             374 non-null    object 
 9   Blood Pressure           374 non-null    object 
 10  Heart Rate               374 non-null    int64  
 11  Daily Steps              374 non-null    int64  
 12  Sleep Disorder           374 non-null    object 
dtypes: float64(1), int64(7), object(5)
memory usage: 38.1+ KB
In [7]:
data['Sleep Disorder'] = data['Sleep Disorder'].fillna('None')
In [8]:
data.head()
Out[8]:
Person ID Gender Age Occupation Sleep Duration Quality of Sleep Physical Activity Level Stress Level BMI Category Blood Pressure Heart Rate Daily Steps Sleep Disorder
0 1 Male 27 Software Engineer 6.1 6 42 6 Overweight 126/83 77 4200 None
1 2 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 None
2 3 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 None
3 4 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
4 5 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
In [9]:
data.isnull().sum()
Out[9]:
Person ID                  0
Gender                     0
Age                        0
Occupation                 0
Sleep Duration             0
Quality of Sleep           0
Physical Activity Level    0
Stress Level               0
BMI Category               0
Blood Pressure             0
Heart Rate                 0
Daily Steps                0
Sleep Disorder             0
dtype: int64
In [10]:
data.duplicated().sum()
Out[10]:
0
In [11]:
data.drop_duplicates()
Out[11]:
Person ID Gender Age Occupation Sleep Duration Quality of Sleep Physical Activity Level Stress Level BMI Category Blood Pressure Heart Rate Daily Steps Sleep Disorder
0 1 Male 27 Software Engineer 6.1 6 42 6 Overweight 126/83 77 4200 None
1 2 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 None
2 3 Male 28 Doctor 6.2 6 60 8 Normal 125/80 75 10000 None
3 4 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
4 5 Male 28 Sales Representative 5.9 4 30 8 Obese 140/90 85 3000 Sleep Apnea
... ... ... ... ... ... ... ... ... ... ... ... ... ...
369 370 Female 59 Nurse 8.1 9 75 3 Overweight 140/95 68 7000 Sleep Apnea
370 371 Female 59 Nurse 8.0 9 75 3 Overweight 140/95 68 7000 Sleep Apnea
371 372 Female 59 Nurse 8.1 9 75 3 Overweight 140/95 68 7000 Sleep Apnea
372 373 Female 59 Nurse 8.1 9 75 3 Overweight 140/95 68 7000 Sleep Apnea
373 374 Female 59 Nurse 8.1 9 75 3 Overweight 140/95 68 7000 Sleep Apnea

374 rows × 13 columns

In [12]:
data.describe()
Out[12]:
Person ID Age Sleep Duration Quality of Sleep Physical Activity Level Stress Level Heart Rate Daily Steps
count 374.000000 374.000000 374.000000 374.000000 374.000000 374.000000 374.000000 374.000000
mean 187.500000 42.184492 7.132086 7.312834 59.171123 5.385027 70.165775 6816.844920
std 108.108742 8.673133 0.795657 1.196956 20.830804 1.774526 4.135676 1617.915679
min 1.000000 27.000000 5.800000 4.000000 30.000000 3.000000 65.000000 3000.000000
25% 94.250000 35.250000 6.400000 6.000000 45.000000 4.000000 68.000000 5600.000000
50% 187.500000 43.000000 7.200000 7.000000 60.000000 5.000000 70.000000 7000.000000
75% 280.750000 50.000000 7.800000 8.000000 75.000000 7.000000 72.000000 8000.000000
max 374.000000 59.000000 8.500000 9.000000 90.000000 8.000000 86.000000 10000.000000
In [13]:
data.columns
Out[13]:
Index(['Person ID', 'Gender', 'Age', 'Occupation', 'Sleep Duration',
       'Quality of Sleep', 'Physical Activity Level', 'Stress Level',
       'BMI Category', 'Blood Pressure', 'Heart Rate', 'Daily Steps',
       'Sleep Disorder'],
      dtype='object')
In [14]:
data['Gender'].unique()
Out[14]:
array(['Male', 'Female'], dtype=object)
In [15]:
gender_count=data['Gender'].value_counts().reset_index()
gender_count
Out[15]:
index Gender
0 Male 189
1 Female 185
In [16]:
data['Age'].describe()
Out[16]:
count    374.000000
mean      42.184492
std        8.673133
min       27.000000
25%       35.250000
50%       43.000000
75%       50.000000
max       59.000000
Name: Age, dtype: float64
In [17]:
age_count=data['Age'].value_counts().reset_index()
age_count
Out[17]:
index Age
0 43 34
1 44 30
2 37 20
3 38 20
4 50 20
5 31 18
6 32 17
7 53 17
8 59 16
9 39 15
10 45 14
11 33 13
12 30 13
13 29 13
14 35 12
15 36 12
16 41 12
17 49 11
18 57 9
19 52 9
20 42 9
21 51 8
22 54 7
23 58 6
24 28 5
25 40 4
26 48 3
27 55 2
28 56 2
29 34 2
30 27 1
In [18]:
data['Occupation'].unique()
Out[18]:
array(['Software Engineer', 'Doctor', 'Sales Representative', 'Teacher',
       'Nurse', 'Engineer', 'Accountant', 'Scientist', 'Lawyer',
       'Salesperson', 'Manager'], dtype=object)
In [19]:
Occupation_count=data['Occupation'].value_counts().reset_index()
Occupation_count
Out[19]:
index Occupation
0 Nurse 73
1 Doctor 71
2 Engineer 63
3 Lawyer 47
4 Teacher 40
5 Accountant 37
6 Salesperson 32
7 Software Engineer 4
8 Scientist 4
9 Sales Representative 2
10 Manager 1
In [20]:
data['Sleep Duration'].describe()
Out[20]:
count    374.000000
mean       7.132086
std        0.795657
min        5.800000
25%        6.400000
50%        7.200000
75%        7.800000
max        8.500000
Name: Sleep Duration, dtype: float64
In [21]:
Sleep_Duration_count=data['Sleep Duration'].value_counts().reset_index()
Sleep_Duration_count
Out[21]:
index Sleep Duration
0 7.2 36
1 6.0 31
2 7.8 28
3 6.5 26
4 6.1 25
5 7.7 24
6 6.6 20
7 7.1 19
8 8.1 15
9 7.3 14
10 8.4 14
11 6.3 13
12 8.5 13
13 8.0 13
14 6.2 12
15 8.2 11
16 7.6 10
17 6.4 9
18 7.9 7
19 7.4 5
20 6.7 5
21 7.5 5
22 6.8 5
23 8.3 5
24 5.9 4
25 6.9 3
26 5.8 2
In [22]:
data['Quality of Sleep'].unique()
Out[22]:
array([6, 4, 7, 5, 8, 9], dtype=int64)
In [23]:
Quality_of_Sleep_count=data['Quality of Sleep'].value_counts().reset_index()
Quality_of_Sleep_count
Out[23]:
index Quality of Sleep
0 8 109
1 6 105
2 7 77
3 9 71
4 5 7
5 4 5
In [24]:
data['Physical Activity Level'].describe()
Out[24]:
count    374.000000
mean      59.171123
std       20.830804
min       30.000000
25%       45.000000
50%       60.000000
75%       75.000000
max       90.000000
Name: Physical Activity Level, dtype: float64
In [25]:
Physical_Activity_Level=data['Physical Activity Level'].value_counts().reset_index()
Physical_Activity_Level
Out[25]:
index Physical Activity Level
0 60 70
1 30 68
2 45 68
3 75 67
4 90 67
5 40 6
6 55 6
7 35 4
8 50 4
9 70 3
10 42 2
11 32 2
12 80 2
13 65 2
14 85 2
15 47 1
In [26]:
data['Stress Level'].unique()
Out[26]:
array([6, 8, 7, 4, 3, 5], dtype=int64)
In [27]:
Stress_Level_counts=data['Stress Level'].value_counts().reset_index()
Stress_Level_counts
Out[27]:
index Stress Level
0 3 71
1 8 70
2 4 70
3 5 67
4 7 50
5 6 46
In [28]:
data['BMI Category'].unique()
Out[28]:
array(['Overweight', 'Normal', 'Obese', 'Normal Weight'], dtype=object)
In [29]:
data['BMI Category']=data['BMI Category'].replace({'Normal':'Normal Weight'})
In [30]:
BMI_Category_count=data['BMI Category'].value_counts().reset_index()
BMI_Category_count
Out[30]:
index BMI Category
0 Normal Weight 216
1 Overweight 148
2 Obese 10
In [31]:
data['Blood Pressure'].unique()
Out[31]:
array(['126/83', '125/80', '140/90', '120/80', '132/87', '130/86',
       '117/76', '118/76', '128/85', '131/86', '128/84', '115/75',
       '135/88', '129/84', '130/85', '115/78', '119/77', '121/79',
       '125/82', '135/90', '122/80', '142/92', '140/95', '139/91',
       '118/75'], dtype=object)
In [32]:
Blood_Pressure_count=data['Blood Pressure'].value_counts().reset_index()
Blood_Pressure_count
Out[32]:
index Blood Pressure
0 130/85 99
1 140/95 65
2 125/80 65
3 120/80 45
4 115/75 32
5 135/90 27
6 140/90 4
7 125/82 4
8 132/87 3
9 128/85 3
10 126/83 2
11 115/78 2
12 139/91 2
13 142/92 2
14 119/77 2
15 135/88 2
16 129/84 2
17 128/84 2
18 131/86 2
19 117/76 2
20 130/86 2
21 118/75 2
22 121/79 1
23 122/80 1
24 118/76 1
In [33]:
Heart_Rate_count=data['Heart Rate'].value_counts().reset_index()
Heart_Rate_count
Out[33]:
index Heart Rate
0 68 94
1 70 76
2 72 69
3 65 67
4 75 36
5 78 5
6 85 3
7 80 3
8 84 2
9 83 2
10 73 2
11 67 2
12 74 2
13 77 2
14 81 2
15 76 2
16 69 2
17 86 2
18 82 1
In [34]:
data['Daily Steps'].describe()
Out[34]:
count      374.000000
mean      6816.844920
std       1617.915679
min       3000.000000
25%       5600.000000
50%       7000.000000
75%       8000.000000
max      10000.000000
Name: Daily Steps, dtype: float64
In [35]:
Daily_Steps_count5=data['Daily Steps'].value_counts().reset_index().head()

Daily_Steps_count5
Out[35]:
index Daily Steps
0 8000 101
1 6000 68
2 5000 68
3 7000 66
4 10000 36
In [36]:
data['Sleep Disorder'].unique()
Out[36]:
array(['None', 'Sleep Apnea', 'Insomnia'], dtype=object)
In [37]:
Sleep_Disorder_count=data['Sleep Disorder'].value_counts().reset_index()
Sleep_Disorder_count
Out[37]:
index Sleep Disorder
0 None 219
1 Sleep Apnea 78
2 Insomnia 77
In [ ]:
In [38]:
import matplotlib.pyplot as plt
In [39]:
fig = px.pie(gender_count, names='Gender',title='Each  Gender and it count  ')
fig.show()
In [40]:
fig = px.pie(Stress_Level_counts ,names='Stress Level',title=" Stress Level")
fig.show()
In [41]:
fig=px.bar(age_count,title='The Age and The Number of peapol in The same Age')

fig.show()
In [42]:
fig=px.bar(Heart_Rate_count,title="the Heart Rate and each count")
fig.show()
In [43]:
fig=px.bar(age_count,title='The Age and The Number of peapol in The same Age')
fig.show()
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